Hybrid acceleration in a processing environment

ABSTRACT

Embodiments of the invention include methods and systems for hybrid acceleration in a processing environment. Aspects of the invention include transmitting, by a first computing system to a second computing system, a request for access to an accelerator. The first computing system receives access information for a plurality of accelerators from the second computing system responsive to the request. The first computing system analyzes the access information for the plurality of accelerators to identify a first accelerator from the plurality of accelerators and offloads a first processing job to the first accelerator utilizing the access information.

BACKGROUND

The present invention generally relates to machine learning, and morespecifically, to hybrid acceleration in a processing environment.

In machine learning and deep learning environments, acceleration isutilized for building and training models quickly when dealing withlarge volumes of data. Machine learning is essentially patternrecognition and machine learning models or algorithms can learn fromlarge amounts of training data to infer predictions on data. Thesemodels allow for results that are reliable and repeatable. Also, thesemodels are sometimes utilized to discover hidden insights into datathrough learning from the historical relationship and/or trends in data.There is much focus on being able to develop machine learning modelsthat are produced faster and, if necessary, re-trained faster fordeployment in data analytics.

SUMMARY

Embodiments of the present invention are directed to acomputer-implemented method for hybrid acceleration in a processingenvironment. A non-limiting example of the computer-implemented methodincludes transmitting, by a first computing system to a second computingsystem, a request for access to an accelerator. The first computingsystem receives access information for a plurality of accelerators fromthe second computing system responsive to the request. The firstcomputing system analyzes the access information for the plurality ofaccelerators to identify a first accelerator from the plurality ofaccelerators and offloads a first processing job to the firstaccelerator utilizing the access information.

Embodiments of the present invention are directed to a system for hybridacceleration in a processing environment. A non-limiting example of thesystem includes transmitting, by a first computing system to a secondcomputing system, a request for access to an accelerator. The firstcomputing system receives access information for a plurality ofaccelerators from the second computing system responsive to the request.The first computing system analyzes the access information for theplurality of accelerators to identify a first accelerator from theplurality of accelerators and offloads a first processing job to thefirst accelerator utilizing the access information.

Embodiments of the invention are directed to a computer program productfor hybrid acceleration in a processing environment, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith. The program instructions areexecutable by a processor to cause the processor to perform a method. Anon-limiting example of the method includes transmitting, by a firstcomputing system to a second computing system, a request for access toan accelerator. The first computing system receives access informationfor a plurality of accelerators from the second computing systemresponsive to the request. The first computing system analyzes theaccess information for the plurality of accelerators to identify a firstaccelerator from the plurality of accelerators and offloads a firstprocessing job to the first accelerator utilizing the accessinformation.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to one or moreembodiments of the present invention;

FIG. 2 depicts abstraction model layers according to one or moreembodiments of the present invention;

FIG. 3 depicts a block diagram of a computer system for use inimplementing one or more embodiments of the present invention;

FIG. 4 depicts a block diagram of a system for hybrid acceleration in aprocessing environment according to one or more embodiments of thepresent invention; and

FIG. 5 depicts a flow diagram of a method for hybrid acceleration in aprocessing environment according to one or more embodiments of theinvention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” may be understood to include any integer numbergreater than or equal to one, i.e. one, two, three, four, etc. The terms“a plurality” may be understood to include any integer number greaterthan or equal to two, i.e. two, three, four, five, etc. The term“connection” may include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and hybrid acceleration 96.

Referring to FIG. 3, there is shown an embodiment of a processing system300 for implementing the teachings herein. In this embodiment, thesystem 300 has one or more central processing units (processors) 21 a,21 b, 21 c, etc. (collectively or generically referred to asprocessor(s) 21). In one or more embodiments, each processor 21 mayinclude a reduced instruction set computer (RISC) microprocessor.Processors 21 are coupled to system memory 34 and various othercomponents via a system bus 33. Read only memory (ROM) 22 is coupled tothe system bus 33 and may include a basic input/output system (BIOS),which controls certain basic functions of system 300.

FIG. 3 further depicts an input/output (I/O) adapter 27 and a networkadapter 26 coupled to the system bus 33. I/O adapter 27 may be a smallcomputer system interface (SCSI) adapter that communicates with a harddisk 23 and/or tape storage drive 25 or any other similar component. I/Oadapter 27, hard disk 23, and tape storage device 25 are collectivelyreferred to herein as mass storage 24. Operating system 40 for executionon the processing system 300 may be stored in mass storage 24. A networkadapter 26 interconnects bus 33 with an outside network 36 enabling dataprocessing system 300 to communicate with other such systems. A screen(e.g., a display monitor) 35 is connected to system bus 33 by displayadaptor 32, which may include a graphics adapter to improve theperformance of graphics intensive applications and a video controller.In one embodiment, adapters 27, 26, and 32 may be connected to one ormore I/O busses that are connected to system bus 33 via an intermediatebus bridge (not shown). Suitable I/O buses for connecting peripheraldevices such as hard disk controllers, network adapters, and graphicsadapters typically include common protocols, such as the PeripheralComponent Interconnect (PCI). Additional input/output devices are shownas connected to system bus 33 via user interface adapter 28 and displayadapter 32. A keyboard 29, mouse 30, and speaker 31 all interconnectedto bus 33 via user interface adapter 28, which may include, for example,a Super I/O chip integrating multiple device adapters into a singleintegrated circuit.

In exemplary embodiments, the processing system 300 includes a graphicsprocessing unit 41. Graphics processing unit 41 is a specializedelectronic circuit designed to manipulate and alter memory to acceleratethe creation of images in a frame buffer intended for output to adisplay. In general, graphics processing unit 41 is very efficient atmanipulating computer graphics and image processing and has a highlyparallel structure that makes it more effective than general-purposeCPUs for algorithms where processing of large blocks of data is done inparallel.

Thus, as configured in FIG. 3, the system 300 includes processingcapability in the form of processors 21, storage capability includingsystem memory 34 and mass storage 24, input means such as keyboard 29and mouse 30, and output capability including speaker 31 and display 35.In one embodiment, a portion of system memory 34 and mass storage 24collectively store an operating system coordinate the functions of thevarious components shown in FIG. 3.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, a hybrid acceleration methodologyfor machine learning models is provided. Machine learning is basicallythe extraction of features from data in order to solve a predictiveproblem. A machine learning (sometimes referred to as “deep learning”)algorithm automatically learns to recognize complex patterns and makeintelligent decisions based on insight generated from learning. Machinelearning techniques include Random Forests, Decision Tree, Ada boost,SVM, k nearest neighbors, and Naive Bayes. Some additional machinelearning techniques include convolutional neural networks (CNNs) andrecurrent neural networks (RNNs). A machine learning model may employimputation techniques to improve a training dataset to achieve a higherconfidence level. Imputation techniques (i.e. substitution of missingvalues) include k nearest neighbor imputation and random forestimputation. More exemplary machine learning techniques includeconverting multi-class classification into a combination of severalbinary classifications. An example of a binary classification that canbe employed includes a one-against-all approach. Some machine learningtechniques may employ a greedy-based sequential binary classificationmodel to classify features. This model uses the one-against-alldecomposition strategy for each binary classification and chooses thebest split as the decomposition for that iteration. This is doneiteratively until all the classes are classified.

Machine learning models are built using training data. Large blocks oftraining data are needed to train machine learning models. In themachine learning and deep learning space, it is becoming very typicallyfor acceleration to be used in order to train systems quickly with largevolumes of data. One acceleration method is to use Graphical ProcessingUnits (GPUs); however, there are also many efforts that will utilizecustom ASIC logic. The ability to quickly leverage advances in thisspace depends on several factors. However, from an applicationprogrammer perspective, an important usage requirement is for theacceleration to be dynamically discovered and have the appropriate workrouted to it. For machine learning and deep learning, the separationcomes between the training of models and the inference of new datapoints using those models.

A graphical processing unit (GPU) is a specialized electronic circuitdesigned to rapidly manipulate and alter memory to accelerate thecreation of images in a frame buffer intended for output to a displaydevice. GPUs are very efficient at manipulating computer graphics andimage processing, and their highly parallel structure makes them moreefficient than general-purpose CPUs for algorithms where the processingof large blocks of data is done in parallel. An application-specificintegrated circuit (ASIC) is an integrated circuit customized for aparticular use, rather than intended for general-purpose use.

In one or more embodiments of the present invention, a system ofdiscovery for accelerators in a hybrid environment is provided. For themainframe computing systems, the acceleration technology can be networkattached servers that contain specialty processing, (e.g., GPUs) and alayer of support that can use the discovery service and route applicablework to the accelerator. The method described allows for accelerationtechnology available on a different computer platform to be connectedand discovered without application awareness on the different computingplatform. The reason for integrating the second computing platform couldbe that it provides unique acceleration that is not available on theprimary platform, for example GPUs or custom ASICs, but the data is allstored and managed on the primary platform.

The application manages the deployment of jobs into the system. Formachine learning and deep learning applications, the two tasks train amodel and then perform inferences using that model.

In machine learning, there is typically a large amount of data that goesinto model training. This model training drives a large amount ofprocessing resources that can take a long elapsed time absent anyacceleration technology. Acceleration technology (e.g., GPUs, ASICs,etc.) allows for faster model training and allows machine learningspecialists to iterate faster during their work. This accelerationtechnology also allows for re-training of models more frequently whichleads to better results from the model. Inference processing is when amodel is deployed and used to score new data that is flowing into thesystem. This can include doing some in-transaction processing. Theinference can be invoked through a programmable API once the model isavailable by existing programs.

FIG. 4 depicts a block diagram of a system for hybrid acceleration in aprocessing environment according to one or more embodiments of thepresent invention. The system 400 includes an end-user software 412located on a computing system much like the computing system describedin FIG. 3. The end-user software 412 includes a discovery module 406 anda job placement module 410. The system 400 also includes a managementserver 404 and one or more hardware device(s) 408.

In one or more embodiments of the invention, the End User Software 412and the Management Server 404 can be implemented on the processingsystem 300 found in FIG. 3. Additionally, the cloud computing system 50can be in wired or wireless electronic communication with one or all ofthe elements of the system 400. Cloud 50 can supplement, support orreplace some or all of the functionality of the elements of the system400. Additionally, some or all of the functionality of the elements ofsystem 400 can be implemented as a node 10 (shown in FIGS. 1 and 2) ofcloud 50. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein.

In one or more embodiments, the hardware device(s) 408 include hardwareaccelerators such as, for example, graphical processing units (GPUs),field programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), and the like. These hardware device(s) are connectedto the management server 404 on devices such as, for example, networkattached servers and the like. The hardware accelerators reside on thesenetwork attached servers that are in electronic communication with themanagement server 404. The management server 404 discovers and indexesthe hardware device(s) 408. The index includes performance andconnectivity information about each of the hardware device(s) 408 suchas, for example, access information, hardware capabilities, buffer size,network connection speed, current processing loads, special permissions,and the like.

In one or more embodiments, the end user software 412 provides theprimitives to perform machine learning model training as well as modeldeployment for inference processing. The end user software 412 can alsoperform steps involving data preparation for the model training thatwould make the data utilized in the model training be correctlyformatted, reduced, and the like before model training can take place.

In one or more embodiments, a request for hybrid acceleration can occurin the following sequence of steps. The management server 404 discoversand indexes hardware device(s) 408 available for training or inferenceprocessing as shown at 401. As described above the hardware device(s)408 are accelerators that can be utilized for machine learning and deeplearning model training and inference processing. It is possible thatthe deployment for inference be based on additional factors, such aslogical proximity to the transactions that will require the result ofthe inference operation. Upon indexing the hardware device(s) 408, themanagement server 404 has the address of each accelerator and canprovide access information to any requesting software applications fromany client devices. When the end user software 412 requires access to anaccelerator for a specific operating, such as training, the discoverymodule 406 can make a discovery call to the management server 404 asshown at 402. The management server 404 communicates back to thediscovery module 406 any available accelerators located on the hardwaredevice(s) 408 as shown at 403. At 403, the communication to thediscovery module 406 includes access information for hardware device(s)408 available. The access information can be provided to the discoverymodule 406 through an API or other similar interface. The job placementmodule 410 analyzes the access information for the accelerators andbased on the job type (e.g., model training, inference processing, etc.)can select an accelerator to offload the machine learning job or anyother processing job as shown at 404.

In one or more embodiments, the job placement module 410 monitors theperformance of the accelerator during processing to ensure that aperformance level is maintained. The performance level can be measuredagainst a performance threshold level for performance requirements suchas processing speed, connection latency, buffering size, and the like.Should the performance level of the current accelerator fall below thethreshold level for any of the performance requirements, the jobplacement module 410 can communicate with the end-user software 412and/or the discovery module to identify additional acceleratorsavailable that meet the job requirements. The discovery module 406 cantransmit a request to the management server 404 for another set ofaccelerators available to take over the model training or any other job.Job requirements for acceleration include but are not limited totraining models for use in machine learning and inference processing.

The job placement module can use historic data about the performance ofjobs to determine the correct threshold for sending a job to anaccelerator. Since the data required for the job requires transfer tothe accelerated server, the total elapsed time of the training job maybe lower if the operating is performed without accelerator but closer tothe data. This threshold can either be set statically by the jobplacement server or discovered over time based on parallel training.

In one or more embodiments, the management server 404 can transmit tothe discovery module 406 a plurality of available of accelerators'access information. Included in this access information can be the indexinformation for each of the accelerators. The job placement module 410selects an accelerator based on the job requirements matched to theperformance information of the accelerator. A machine learning job canbe assigned to only one accelerator or can be distributed acrossmultiple accelerators depending on the job type and performancerequirements needed to carry out the job. Also, as accelerators comeonline, the discovery module 406 can continue to request availableaccelerators from the management server 404 and provide any new orrecently available accelerators to the job placement module 410. The jobplacement module 410 continues to monitor the usage of the one or moreaccelerators and can switch all or part of a job to other acceleratorsas the performance requirements are needed and/or if performance levelsfall below a threshold.

In one or more embodiments, the job placement module 410 continuouslymonitors the accelerators to check for latency and connection issues aswell as performance levels. Should any performance or connection issuesarise, a failure can occur and the job placement module 410 can routethe job back to another accelerator or to a resource located on thecomputer system running the end-user software 412, if available.

In one or more embodiments, the computing system running the end usersoftware 412 does not have access permissions to any of the hardwaredevice(s) 408 until the access information is provided by the managementserver 404. In another embodiment, the computing system running theend-user software 412 does have access permission to the hardwaredevice(s) 408 before the access information is provided by themanagement server 404. In one or more embodiments, the hardwaredevice(s) 408 reside on the management server 404. In anotherembodiment, the hardware device(s) 408 do not reside on the managementserver 404.

FIG. 5 depicts a flow diagram of a method for hybrid acceleration in aprocessing environment according to one or more embodiments of theinvention. The method 500 includes transmitting, by a first computingsystem to a second computing system, a request for access to anaccelerator, as shown at block 502. At block 504, the method 500includes receiving, by the first computing system, access informationfor a plurality of accelerators from the second computing systemresponsive to the request. The method 500, at block 506, includesanalyzing, by the first computing system, the access information for theplurality of accelerators to identify a first accelerator from theplurality of accelerators. And at block 508, the method 500 includesoffloading, by the first computing system, a first processing job to thefirst accelerator utilizing the access information.

Additional processes may also be included. It should be understood thatthe processes depicted in FIG. 5 represent illustrations, and that otherprocesses may be added or existing processes may be removed, modified,or rearranged without departing from the scope and spirit of the presentdisclosure.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user' s computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

1-10. (canceled)
 11. A system for hybrid acceleration in a processingenvironment, the system having a processor coupled to a memory on afirst computing system, the processor configured to: transmit, to asecond computing system, a request for access to an accelerator; receiveaccess information for a plurality of accelerators from the secondcomputing system responsive to the request; analyze the accessinformation for the plurality of accelerators to identify a firstaccelerator from the plurality of accelerators; and offload a firstprocessing job to the first accelerator utilizing the accessinformation.
 12. The system of claim 11, wherein the processor isfurther configured to: analyze the access information for the pluralityof accelerators to identify a second accelerator from the plurality ofaccelerators; and offload a second processing job to the secondaccelerator utilizing the access information.
 13. The system of claim11, wherein the processor is further configured to: monitor aperformance level of the first accelerator.
 14. The system of claim 13,wherein the processor is further configured to: based at least in parton a determination that the performance level of the first acceleratoris below a threshold performance level, analyze the access informationfor the plurality of accelerators to identify a third accelerator fromthe plurality of accelerators; and offload the first processing job tothe third accelerator utilizing the access information.
 15. The systemof claim 11, wherein the request for access to the accelerator includesone or more job requirements; and wherein the second computing systemprovides the plurality of accelerators based at least in part on the oneor more job requirements.
 16. A computer program product for hybridacceleration in a processing environment, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the processor to perform: transmitting, by a firstcomputing system to a second computing system, a request for access toan accelerator; receiving, by the first computing system, accessinformation for a plurality of accelerators from the second computingsystem responsive to the request; analyzing, by the first computingsystem, the access information for the plurality of accelerators toidentify a first accelerator from the plurality of accelerators; andoffloading, by the first computing system, a first processing job to thefirst accelerator utilizing the access information.
 17. The computerprogram product of claim 16 further comprising: analyzing, by the firstcomputing system, the access information for the plurality ofaccelerators to identify a second accelerator from the plurality ofaccelerators; and offloading a second processing job to the secondaccelerator utilizing the access information.
 18. The computer programproduct of claim 16 further comprising: monitoring, by the firstcomputing system, a performance level of the first accelerator.
 19. Thecomputer program product of claim 18 further comprising: based at leastin part on a determination that the performance level of the firstaccelerator is below a threshold performance level, analyzing the accessinformation for the plurality of accelerators to identify a thirdaccelerator from the plurality of accelerators; and offloading the firstprocessing job to the third accelerator utilizing the accessinformation.
 20. The computer program product of claim 16, wherein therequest for access to the accelerator includes one or more jobrequirements; and wherein the second computing system provides theplurality of accelerators based at least in part on the one or more jobrequirements.